Inactivation of Histone Deacetylase 1 (HDAC1) But Not HDAC2 Is Required for the Glucocorticoid-Dependent CCAAT/Enhancer-Binding Protein α (C/EBPα) Expression and Preadipocyte Differentiation
Bibliographic record
Abstract
Several drugs currently used in the management of mood disorders, epilepsy (ie, valproic acid), or the control of inflammation (ie, corticosteroids) have been shown to promote visceral obesity in humans by increasing the number of newly formed adipocytes. Valproic acid is classified as a nonspecific histone deacetylase (HDAC) inhibitor, along with trichostatin A and butyric acid. In vitro experiments have demonstrated that such molecules greatly enhance the rate of preadipocyte differentiation, similarly to the effect of corticosteroids. The glucocorticoid receptor stimulates adipogenesis in part by enhancing the transcription of C/ebpa through the titration, and subsequent degradation, of HDAC1 from the C/ebpα promoter. There is, however, controversy in the literature as to the role of HDACs during adipogenesis. In this study, we sought to demonstrate, using 2 different strategies, the definite role of HDAC1 in adipogenesis. By using small interference RNA-mediated knockdown of HDAC1 and by generating an enzymatically inactive HDAC1D181A by site-directed mutagenesis, we were able to show that HDAC1, but not HDAC2, suppresses glucocorticoid receptor-potentiated preadipocyte differentiation by decreasing CCAAT/enhancer-binding protein (C/ebp)α and Pparγ expression levels at the onset of differentiation. Finally, we demonstrate that HDAC1D181A acts as a dominant negative mutant of HDAC1 during adipogenesis by modulating C/EBPβ transcriptional activity on the C/ebpα promoter.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".